The invention relates to an image retrieval system which includes a database with candidate images, an entry unit for entering a query image, and a first histogram unit for deriving a first query color histogram from the query image.
A second histogram unit derives a first candidate color histogram from a particular candidate image. Also a determining unit determines a first similarity between the particular candidate image and the query image on the basis of the first candidate color histogram and the first query color histogram, and a retrieval unit retrieves of the particular candidate image.
The invention further relates to a method for determining a similarity between a candidate image and a query image.
A first step obtains the query image, a second step derives a query color histogram from the query image, a third step obtains a candidate color histogram from the candidate image, and a determining step determines the similarity between the particular candidate image and the query image on the basis of the candidate color histogram and the query color histogram.
Image retrieval systems are of importance for applications that involve large collections of images. Professional applications include broadcast stations where a piece of a video may be identified through a set of shots and where a shot of video is to be retrieved according to a given image. Also movie producers must be able to find back scenes from among a large number of scenes. Furthermore, art museums have large collections of images, from their paintings, photos and drawings, and must be able to retrieve images on the basis of some criterion. Consumer applications include maintaining collections of slides, photos and videos, from which the user must be able to find back items.
An image retrieval system and a method as described above, are known from the article "Tools and Techniques for Color Image Retrieval", John R. Smith and Shih-Fu Chang, Proc. SPIE--Int. Soc. Opt. Eng (USA), Vol. 2670, pp. 426-437. The image retrieval system includes a database with a large number of images. A user searching for a particular image specifies a query image as to how the retrieved image or images should lock like. Then the system compares the stored images with the query image and ranks the stored image according to their similarity with the query image. The ranking results are presented to the user who may retrieve one or more of the images. The comparison of the query image with a stored image to determine the similarity may be based on a number of features derived from the respective images. The article describes the usage of a color histogram as such a comparison feature. When using the RGB (Red, Green and Blue) representation of an image, a color histogram is computed by quantizing the colors within the image and counting the number of pixels of each color. To determine the similarity, a number of techniques are described to compare the two color histograms of the respective images. The histogram euclidean distance is a simple measure calculated by comparing identical bins in respective histograms. No cross-wise comparison is made between different bins which represent perceptually similar colors. Furthermore, techniques for determining a histogram intersection and techniques for determining a histogram quadratic distance are described. As an alternative to the histogram techniques, a comparison technique based on color sets is described. In this technique the color of a pixel is compared with a predetermined threshold. If the color is below the threshold, the pixel does not become a member of the set and otherwise it does become a member. A disadvantage is that a large number of pixels, all below the threshold, will not contribute in the comparison in any way. Furthermore, there is no discrimination between values above the threshold. The prior art techniques for determining the similarity between the candidate image and the query image are complex to execute and/or are occasionally not adequate enough.